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demo.dump
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/fantom845/execute-2.0-symphony/blob/main/Sales_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"source": [
"#!pip install pandas numpy seaborn matplotlib klib dtale scikit-learn joblib pandas-profiling\n",
"#!pip install dtale"
],
"metadata": {
"id": "CsZCgt65O-7L"
},
"id": "CsZCgt65O-7L",
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
],
"metadata": {
"id": "HVFxcbjyPCkt"
},
"id": "HVFxcbjyPCkt",
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df_train= pd.read_csv('/content/Train.csv')\n",
"df_test= pd.read_csv('/content/Test.csv')"
],
"metadata": {
"id": "Q_CAMGeKQcAc"
},
"id": "Q_CAMGeKQcAc",
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df_train.head()"
],
"metadata": {
"id": "v6unja8YSJ3I",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 357
},
"outputId": "2495a50b-fb02-4830-8df7-b7cb462e84d8"
},
"id": "v6unja8YSJ3I",
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Item_Identifier Item_Weight Item_Fat_Content Item_Visibility \\\n",
"0 FDA15 9.30 Low Fat 0.016047 \n",
"1 DRC01 5.92 Regular 0.019278 \n",
"2 FDN15 17.50 Low Fat 0.016760 \n",
"3 FDX07 19.20 Regular 0.000000 \n",
"4 NCD19 8.93 Low Fat 0.000000 \n",
"\n",
" Item_Type Item_MRP Outlet_Identifier \\\n",
"0 Dairy 249.8092 OUT049 \n",
"1 Soft Drinks 48.2692 OUT018 \n",
"2 Meat 141.6180 OUT049 \n",
"3 Fruits and Vegetables 182.0950 OUT010 \n",
"4 Household 53.8614 OUT013 \n",
"\n",
" Outlet_Establishment_Year Outlet_Size Outlet_Location_Type \\\n",
"0 1999 Medium Tier 1 \n",
"1 2009 Medium Tier 3 \n",
"2 1999 Medium Tier 1 \n",
"3 1998 NaN Tier 3 \n",
"4 1987 High Tier 3 \n",
"\n",
" Outlet_Type Item_Outlet_Sales \n",
"0 Supermarket Type1 3735.1380 \n",
"1 Supermarket Type2 443.4228 \n",
"2 Supermarket Type1 2097.2700 \n",
"3 Grocery Store 732.3800 \n",
"4 Supermarket Type1 994.7052 "
],
"text/html": [
"\n",
" <div id=\"df-33b77f5b-5173-408e-a161-78ce9f7a586b\">\n",
" <div class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Item_Identifier</th>\n",
" <th>Item_Weight</th>\n",
" <th>Item_Fat_Content</th>\n",
" <th>Item_Visibility</th>\n",
" <th>Item_Type</th>\n",
" <th>Item_MRP</th>\n",
" <th>Outlet_Identifier</th>\n",
" <th>Outlet_Establishment_Year</th>\n",
" <th>Outlet_Size</th>\n",
" <th>Outlet_Location_Type</th>\n",
" <th>Outlet_Type</th>\n",
" <th>Item_Outlet_Sales</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>FDA15</td>\n",
" <td>9.30</td>\n",
" <td>Low Fat</td>\n",
" <td>0.016047</td>\n",
" <td>Dairy</td>\n",
" <td>249.8092</td>\n",
" <td>OUT049</td>\n",
" <td>1999</td>\n",
" <td>Medium</td>\n",
" <td>Tier 1</td>\n",
" <td>Supermarket Type1</td>\n",
" <td>3735.1380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>DRC01</td>\n",
" <td>5.92</td>\n",
" <td>Regular</td>\n",
" <td>0.019278</td>\n",
" <td>Soft Drinks</td>\n",
" <td>48.2692</td>\n",
" <td>OUT018</td>\n",
" <td>2009</td>\n",
" <td>Medium</td>\n",
" <td>Tier 3</td>\n",
" <td>Supermarket Type2</td>\n",
" <td>443.4228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>FDN15</td>\n",
" <td>17.50</td>\n",
" <td>Low Fat</td>\n",
" <td>0.016760</td>\n",
" <td>Meat</td>\n",
" <td>141.6180</td>\n",
" <td>OUT049</td>\n",
" <td>1999</td>\n",
" <td>Medium</td>\n",
" <td>Tier 1</td>\n",
" <td>Supermarket Type1</td>\n",
" <td>2097.2700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>FDX07</td>\n",
" <td>19.20</td>\n",
" <td>Regular</td>\n",
" <td>0.000000</td>\n",
" <td>Fruits and Vegetables</td>\n",
" <td>182.0950</td>\n",
" <td>OUT010</td>\n",
" <td>1998</td>\n",
" <td>NaN</td>\n",
" <td>Tier 3</td>\n",
" <td>Grocery Store</td>\n",
" <td>732.3800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NCD19</td>\n",
" <td>8.93</td>\n",
" <td>Low Fat</td>\n",
" <td>0.000000</td>\n",
" <td>Household</td>\n",
" <td>53.8614</td>\n",
" <td>OUT013</td>\n",
" <td>1987</td>\n",
" <td>High</td>\n",
" <td>Tier 3</td>\n",
" <td>Supermarket Type1</td>\n",
" <td>994.7052</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33b77f5b-5173-408e-a161-78ce9f7a586b')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-33b77f5b-5173-408e-a161-78ce9f7a586b button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-33b77f5b-5173-408e-a161-78ce9f7a586b');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"source": [
"#df_test"
],
"metadata": {
"id": "yqLOjwGUUEWR"
},
"id": "yqLOjwGUUEWR",
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df_train.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Rer0Y7nUUGxh",
"outputId": "93094893-dd6a-481f-dbba-30d007a84ea5"
},
"id": "Rer0Y7nUUGxh",
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(8523, 12)"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"source": [
"df_train.isnull().sum()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "quHNCog5UI4Q",
"outputId": "0915d54e-0c92-4a22-a3c9-cf7254dbeb37"
},
"id": "quHNCog5UI4Q",
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Item_Identifier 0\n",
"Item_Weight 1463\n",
"Item_Fat_Content 0\n",
"Item_Visibility 0\n",
"Item_Type 0\n",
"Item_MRP 0\n",
"Outlet_Identifier 0\n",
"Outlet_Establishment_Year 0\n",
"Outlet_Size 2410\n",
"Outlet_Location_Type 0\n",
"Outlet_Type 0\n",
"Item_Outlet_Sales 0\n",
"dtype: int64"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"df_train.info()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "G72diEuKUK51",
"outputId": "31250cf7-19b0-4b90-dae9-f9386d0108a0"
},
"id": "G72diEuKUK51",
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 8523 entries, 0 to 8522\n",
"Data columns (total 12 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Item_Identifier 8523 non-null object \n",
" 1 Item_Weight 7060 non-null float64\n",
" 2 Item_Fat_Content 8523 non-null object \n",
" 3 Item_Visibility 8523 non-null float64\n",
" 4 Item_Type 8523 non-null object \n",
" 5 Item_MRP 8523 non-null float64\n",
" 6 Outlet_Identifier 8523 non-null object \n",
" 7 Outlet_Establishment_Year 8523 non-null int64 \n",
" 8 Outlet_Size 6113 non-null object \n",
" 9 Outlet_Location_Type 8523 non-null object \n",
" 10 Outlet_Type 8523 non-null object \n",
" 11 Item_Outlet_Sales 8523 non-null float64\n",
"dtypes: float64(4), int64(1), object(7)\n",
"memory usage: 799.2+ KB\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"df_train.describe()"
],
"metadata": {
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},
"id": "8FeNDz6fUbk0",
"outputId": "1882fea6-dd01-4069-b16f-0aae4dd6a6ef"
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"id": "8FeNDz6fUbk0",
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Item_Weight Item_Visibility Item_MRP Outlet_Establishment_Year \\\n",
"count 7060.000000 8523.000000 8523.000000 8523.000000 \n",
"mean 12.857645 0.066132 140.992782 1997.831867 \n",
"std 4.643456 0.051598 62.275067 8.371760 \n",
"min 4.555000 0.000000 31.290000 1985.000000 \n",
"25% 8.773750 0.026989 93.826500 1987.000000 \n",
"50% 12.600000 0.053931 143.012800 1999.000000 \n",
"75% 16.850000 0.094585 185.643700 2004.000000 \n",
"max 21.350000 0.328391 266.888400 2009.000000 \n",
"\n",
" Item_Outlet_Sales \n",
"count 8523.000000 \n",
"mean 2181.288914 \n",
"std 1706.499616 \n",
"min 33.290000 \n",
"25% 834.247400 \n",
"50% 1794.331000 \n",
"75% 3101.296400 \n",
"max 13086.964800 "
],
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" <th>count</th>\n",
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" <td>8523.000000</td>\n",
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" <td>0.066132</td>\n",
" <td>140.992782</td>\n",
" <td>1997.831867</td>\n",
" <td>2181.288914</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>4.643456</td>\n",
" <td>0.051598</td>\n",
" <td>62.275067</td>\n",
" <td>8.371760</td>\n",
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" <td>1999.000000</td>\n",
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},
"metadata": {},
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}
]
},
{
"cell_type": "code",
"source": [
"### Item_Weight is numerical column so we fill it with Mean Imputation"
],
"metadata": {
"id": "OtkMwV95U276"
},
"id": "OtkMwV95U276",
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df_train['Item_Weight'].describe()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LJiD_QaeVG8S",
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},
"id": "LJiD_QaeVG8S",
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"count 7060.000000\n",
"mean 12.857645\n",
"std 4.643456\n",
"min 4.555000\n",
"25% 8.773750\n",
"50% 12.600000\n",
"75% 16.850000\n",
"max 21.350000\n",
"Name: Item_Weight, dtype: float64"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"df_train['Item_Weight'].fillna(df_train['Item_Weight'].mean(),inplace=True)\n",
"df_test['Item_Weight'].fillna(df_test['Item_Weight'].mean(),inplace=True)"
],
"metadata": {
"id": "Lze7_184VLnG"
},
"id": "Lze7_184VLnG",
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df_train.isnull().sum()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GoclKMBJVR94",
"outputId": "60038f8d-5416-45c3-8fb0-2071819989ed"
},
"id": "GoclKMBJVR94",
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Item_Identifier 0\n",
"Item_Weight 0\n",
"Item_Fat_Content 0\n",
"Item_Visibility 0\n",
"Item_Type 0\n",
"Item_MRP 0\n",
"Outlet_Identifier 0\n",
"Outlet_Establishment_Year 0\n",
"Outlet_Size 2410\n",
"Outlet_Location_Type 0\n",
"Outlet_Type 0\n",
"Item_Outlet_Sales 0\n",
"dtype: int64"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"source": [
"df_train['Item_Weight'].describe()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gsJ13OH4VUvi",
"outputId": "73f54930-78de-4424-8061-81373070433d"
},
"id": "gsJ13OH4VUvi",
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"count 8523.000000\n",
"mean 12.857645\n",
"std 4.226124\n",
"min 4.555000\n",
"25% 9.310000\n",
"50% 12.857645\n",
"75% 16.000000\n",
"max 21.350000\n",
"Name: Item_Weight, dtype: float64"
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"source": [
"### Outlet_Size is catagorical column so we fill it with Mode Imputation"
],
"metadata": {
"id": "qCnGYMJXVXUd"
},
"id": "qCnGYMJXVXUd",
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df_train['Outlet_Size'].value_counts()"
],
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"id": "0Tkxkko7WV_x",
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Medium 2793\n",
"Small 2388\n",
"High 932\n",
"Name: Outlet_Size, dtype: int64"
]
},
"metadata": {},
"execution_count": 16
}
]
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{
"cell_type": "code",
"source": [
"df_train['Outlet_Size'].mode()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
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"id": "04yHFYtjWVwe",
"outputId": "588754ef-6a5b-43eb-b8c5-3690160e0750"
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"id": "04yHFYtjWVwe",
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 Medium\n",
"dtype: object"
]
},
"metadata": {},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"source": [
"df_train['Outlet_Size'].fillna(df_train['Outlet_Size'].mode()[0],inplace=True)\n",
"df_test['Outlet_Size'].fillna(df_test['Outlet_Size'].mode()[0],inplace=True)"
],
"metadata": {
"id": "yGrU5iIYWbVu"
},
"id": "yGrU5iIYWbVu",
"execution_count": 18,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df_train.isnull().sum()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gxVkM4z2WeCb",
"outputId": "78c49714-4e70-4420-9480-797cb2af9437"
},
"id": "gxVkM4z2WeCb",
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Item_Identifier 0\n",
"Item_Weight 0\n",
"Item_Fat_Content 0\n",
"Item_Visibility 0\n",
"Item_Type 0\n",
"Item_MRP 0\n",
"Outlet_Identifier 0\n",
"Outlet_Establishment_Year 0\n",
"Outlet_Size 0\n",
"Outlet_Location_Type 0\n",
"Outlet_Type 0\n",
"Item_Outlet_Sales 0\n",
"dtype: int64"
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"source": [
"df_test.isnull().sum()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ygc48AefWgjD",
"outputId": "98917444-27a1-4040-a903-242024fdc51f"
},
"id": "ygc48AefWgjD",
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Item_Identifier 0\n",
"Item_Weight 0\n",
"Item_Fat_Content 0\n",
"Item_Visibility 0\n",
"Item_Type 0\n",
"Item_MRP 0\n",
"Outlet_Identifier 0\n",
"Outlet_Establishment_Year 0\n",
"Outlet_Size 0\n",
"Outlet_Location_Type 0\n",
"Outlet_Type 0\n",
"dtype: int64"
]
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"source": [
"# Selecting features based on general requirements"
],
"metadata": {
"id": "Iu-0ZOLBVvCD"
},
"id": "Iu-0ZOLBVvCD",
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df_train.drop(['Item_Identifier','Outlet_Identifier'],axis=1,inplace=True)\n",
"df_test.drop(['Item_Identifier','Outlet_Identifier'],axis=1,inplace=True)"
],
"metadata": {
"id": "K3KH6HVeVv6Q"
},
"id": "K3KH6HVeVv6Q",
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df_train"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 661
},
"id": "POSWZGsGV0_O",
"outputId": "816b9bba-1a32-4fc3-83ce-1f4ad0f3d995"
},
"id": "POSWZGsGV0_O",
"execution_count": 23,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Item_Weight Item_Fat_Content Item_Visibility Item_Type \\\n",
"0 9.300 Low Fat 0.016047 Dairy \n",
"1 5.920 Regular 0.019278 Soft Drinks \n",
"2 17.500 Low Fat 0.016760 Meat \n",
"3 19.200 Regular 0.000000 Fruits and Vegetables \n",
"4 8.930 Low Fat 0.000000 Household \n",
"... ... ... ... ... \n",
"8518 6.865 Low Fat 0.056783 Snack Foods \n",
"8519 8.380 Regular 0.046982 Baking Goods \n",
"8520 10.600 Low Fat 0.035186 Health and Hygiene \n",
"8521 7.210 Regular 0.145221 Snack Foods \n",
"8522 14.800 Low Fat 0.044878 Soft Drinks \n",
"\n",
" Item_MRP Outlet_Establishment_Year Outlet_Size Outlet_Location_Type \\\n",
"0 249.8092 1999 Medium Tier 1 \n",
"1 48.2692 2009 Medium Tier 3 \n",
"2 141.6180 1999 Medium Tier 1 \n",
"3 182.0950 1998 Medium Tier 3 \n",
"4 53.8614 1987 High Tier 3 \n",
"... ... ... ... ... \n",
"8518 214.5218 1987 High Tier 3 \n",